| treatment | age_group | patient_id | sample | Sequence |
|---|---|---|---|---|
| 0 | 2 | 3 | 160008699_3_0_S5 | 1 |
| 1 | 2 | 3 | 160008699_3_8_S6 | 2 |
| 0 | 2 | 4 | 290001824_4_0_S7 | 3 |
| 1 | 2 | 4 | 290001824_4_8_S8 | 4 |
| 0 | 1 | 17 | 330001842_17_0_S31 | 5 |
| 1 | 1 | 17 | 330001842_17_8_S32 | 6 |
| 0 | 0 | 5 | 470009458_5_0_S9 | 7 |
| 1 | 0 | 5 | 470009458_5_4_S10 | 8 |
| 0 | 1 | 13 | 660009823_13_0_S25 | 9 |
| 1 | 1 | 13 | 660009823_13_8_S26 | 10 |
| 0 | 0 | 11 | 770004766_11_0_S21 | 11 |
| 1 | 0 | 11 | 770004766_11_8_S22 | 12 |
| 0 | 1 | 2 | 830001304_2_0_S3 | 13 |
| 1 | 1 | 2 | 830001304_2_4_S4 | 14 |
| 0 | 2 | 12 | 830002078_12_0_S23 | 15 |
| 1 | 2 | 12 | 830002078_12_8_S24 | 16 |
| 0 | 2 | 9 | 880001252_9_0_S17 | 17 |
| 1 | 2 | 9 | 880001252_9_8_S18 | 18 |
| 0 | 0 | 8 | 940004357_8_0_S15 | 19 |
| 1 | 0 | 8 | 940004357_8_8_S16 | 20 |
| 0 | 1 | 7 | 970002731_7_0_S13 | 21 |
| 1 | 1 | 7 | 970002731_7_4_S14 | 22 |
| 0 | 0 | 10 | 980007758_10_0_S19 | 23 |
| 1 | 0 | 10 | 980007758_10_8_S20 | 24 |
Soft threshold = 16
soft threshold = 16
.
Modules = 29
29 modules in total
.
| Positive modules | Spearman correlation (p-value) |
|---|---|
| lightgreen (152 genes) | 0.14 (0.1) |
| Negative modules | Spearman correlation (p-value) |
|---|---|
| darkred (63 genes) | -0.12 (0.2) |
| midnightblue (303 genes) | -0.1 (0.2) |
Alpha = 1
Nested cross validation
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Tuned lambda value:
## 0.04302683
##
## Call: cv.glmnet(x = x, y = y, weights = ..2, foldid = foldid, alpha = tail(alphaSet, 1), family = ..1, penalty.factor = ..3)
##
## Measure: Binomial Deviance
##
## Lambda Index Measure SE Nonzero
## min 0.04303 41 1.042 0.2130 10
## 1se 0.08645 26 1.244 0.1541 7
## Non-zero Coefficients:
## ENSG00000152894 ENSG00000084072 ENSG00000101057 ENSG00000173898 ENSG00000026559 ENSG00000058091 ENSG00000134532 ENSG00000112232 ENSG00000166833 ENSG00000168502
| ensembl_gene_id | external_gene_name |
|---|---|
| ENSG00000026559 | KCNG1 |
| ENSG00000058091 | CDK14 |
| ENSG00000084072 | PPIE |
| ENSG00000101057 | MYBL2 |
| ENSG00000112232 | KHDRBS2 |
| ENSG00000134532 | SOX5 |
| ENSG00000152894 | PTPRK |
| ENSG00000166833 | NAV2 |
| ENSG00000168502 | MTCL1 |
| ENSG00000173898 | SPTBN2 |
## Reference
## Predicted 0 1
## 0 10 3
## 1 2 9
## AUC Accuracy Balanced accuracy
## 0.7708333 0.7916667 0.7916667
## Warning: Setting row names on a tibble is deprecated.
## Setting row names on a tibble is deprecated.
Alpha = 1
Nested cross validation
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Tuned lambda value:
## 0.1464232
##
## Call: cv.glmnet(x = x, y = y, weights = ..2, foldid = foldid, alpha = tail(alphaSet, 1), family = ..1, penalty.factor = ..3)
##
## Measure: Binomial Deviance
##
## Lambda Index Measure SE Nonzero
## min 0.1464 10 1.515 0.12399 4
## 1se 0.2225 1 1.524 0.07232 0
## Non-zero Coefficients:
## ENSG00000279982 ENSG00000271550 ENSG00000272502 ENSG00000169519
##
## 0 1
## 0 12 0
## 1 0 12
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.000000e+00 1.000000e+00 8.575264e-01 1.000000e+00 5.000000e-01
## AccuracyPValue McnemarPValue
## 5.960464e-08 NaN
| ensembl_gene_id | external_gene_name |
|---|---|
| ENSG00000169519 | METTL15 |
| ENSG00000271550 | BNIP3P11 |
| ENSG00000272502 | ENSG00000272502 |
| ENSG00000279982 | ENSG00000279982 |
## Warning: Setting row names on a tibble is deprecated.
## Setting row names on a tibble is deprecated.
Alpha = 1
Nested cross validation
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Warning in lognet(xd, is.sparse, ix, jx, y, weights, offset, alpha, nobs, : one
## multinomial or binomial class has fewer than 8 observations; dangerous ground
## Tuned lambda value:
## 0.2217997
##
## Call: cv.glmnet(x = x, y = y, weights = ..2, foldid = foldid, alpha = tail(alphaSet, 1), family = ..1, penalty.factor = ..3)
##
## Measure: Binomial Deviance
##
## Lambda Index Measure SE Nonzero
## min 0.2218 2 1.509 0.09580 1
## 1se 0.2324 1 1.509 0.09087 0
## Non-zero Coefficients:
## ENSG00000186073
##
## 0 1
## 0 12 0
## 1 0 12
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.000000e+00 1.000000e+00 8.575264e-01 1.000000e+00 5.000000e-01
## AccuracyPValue McnemarPValue
## 5.960464e-08 NaN
| ensembl_gene_id | external_gene_name |
|---|---|
| ENSG00000186073 | CDIN1 |
## Warning: Setting row names on a tibble is deprecated.
## Setting row names on a tibble is deprecated.
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Warning: Option grouped=FALSE enforced in cv.glmnet, since < 3 observations per
## fold
## Tuned lambda value:
## 0.02243423
##
## Call: cv.glmnet(x = x, y = y, weights = ..2, foldid = foldid, alpha = tail(alphaSet, 1), family = ..1, penalty.factor = ..3)
##
## Measure: Binomial Deviance
##
## Lambda Index Measure SE Nonzero
## min 0.02243 28 0.9762 0.3644 10
## 1se 0.19064 5 1.2901 0.0605 2
## Non-zero Coefficients:
## ENSG00000152894 ENSG00000186073 ENSG00000112232 ENSG00000173898 ENSG00000058091 ENSG00000084072 ENSG00000166833 ENSG00000101057 ENSG00000271550 ENSG00000134532
## Reference
## Predicted 0 1
## 0 10 3
## 1 2 9
## AUC Accuracy Balanced accuracy
## 0.8611111 0.7916667 0.7916667
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Call:
## glm(formula = formula_str, family = binomial, data = data.frame(final_model_matrix))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.098e+00 1.006e+05 0 1
## ENSG00000152894 1.064e+01 2.610e+05 0 1
## ENSG00000186073 -2.001e+01 1.661e+05 0 1
## ENSG00000112232 2.255e+01 1.773e+05 0 1
## ENSG00000173898 -3.164e+01 2.157e+05 0 1
## ENSG00000058091 2.196e+01 2.018e+05 0 1
## ENSG00000084072 -1.369e+00 1.491e+05 0 1
## ENSG00000166833 -3.067e+00 1.208e+05 0 1
## ENSG00000101057 -7.221e-01 2.505e+05 0 1
## ENSG00000271550 8.555e-01 2.179e+05 0 1
## ENSG00000134532 2.729e+00 1.245e+05 0 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 3.3271e+01 on 23 degrees of freedom
## Residual deviance: 3.8444e-10 on 13 degrees of freedom
## AIC: 22
##
## Number of Fisher Scoring iterations: 25
## Implementation: ROI | Solver: lpsolve
## Separation: TRUE
## Existence of maximum likelihood estimates
## (Intercept) ENSG00000152894 ENSG00000186073 ENSG00000112232 ENSG00000173898
## -Inf Inf -Inf Inf -Inf
## ENSG00000058091 ENSG00000084072 ENSG00000166833 ENSG00000101057 ENSG00000271550
## Inf -Inf Inf -Inf -Inf
## ENSG00000134532
## -Inf
## 0: finite value, Inf: infinity, -Inf: -infinity
## bayesglm(formula = formula_str, family = binomial(link = "logit"),
## data = as.data.frame(final_model_matrix))
## coef.est coef.se
## (Intercept) -0.12 0.82
## ENSG00000152894 1.90 1.05
## ENSG00000186073 -0.90 0.92
## ENSG00000112232 0.98 0.75
## ENSG00000173898 -0.73 0.85
## ENSG00000058091 0.72 0.78
## ENSG00000084072 -0.46 0.81
## ENSG00000166833 -0.38 0.67
## ENSG00000101057 -0.49 0.78
## ENSG00000271550 -0.46 0.84
## ENSG00000134532 -0.42 0.74
## ---
## n = 24, k = 11
## residual deviance = 4.4, null deviance = 33.3 (difference = 28.9)
##
## Call: bayesglm(formula = formula_str, family = binomial(link = "logit"),
## data = as.data.frame(final_model_matrix), method = "detect_separation")
##
## Coefficients:
## (Intercept) ENSG00000152894 ENSG00000186073 ENSG00000112232
## -0.1212 1.9042 -0.9044 0.9826
## ENSG00000173898 ENSG00000058091 ENSG00000084072 ENSG00000166833
## -0.7303 0.7199 -0.4559 -0.3832
## ENSG00000101057 ENSG00000271550 ENSG00000134532
## -0.4893 -0.4556 -0.4180
##
## Degrees of Freedom: 23 Total (i.e. Null); 13 Residual
## Null Deviance: 33.27
## Residual Deviance: 4.371 AIC: 26.37
Firth’s bias reduction method, equivalent to penalization of the log-likelihood
## logistf(formula = formula_str, data = as.data.frame(final_model_matrix))
##
## Model fitted by Penalized ML
## Coefficients:
## coef se(coef) lower 0.95 upper 0.95 Chisq
## (Intercept) -0.12162486 0.4778238 -1.951730 1.132856 0.04654081
## ENSG00000152894 0.80908839 0.9235939 -1.807387 5.566714 0.56450734
## ENSG00000186073 -0.38336802 0.8365246 -3.149846 1.940889 0.15791745
## ENSG00000112232 0.75311207 0.8088889 -1.108937 4.582622 0.68644923
## ENSG00000173898 -0.71874510 0.6776823 -4.860134 1.561141 0.77934305
## ENSG00000058091 0.93825921 0.7412681 -1.345513 4.437082 0.91025159
## ENSG00000084072 -0.15907838 0.7356271 -2.756363 1.884979 0.03434081
## ENSG00000166833 -0.41668886 0.4749537 -2.084143 1.162984 0.56991526
## ENSG00000101057 -0.64074898 0.7742647 -4.848279 1.731083 0.47489122
## ENSG00000271550 -0.19599018 0.9240849 -3.468708 2.428395 0.03374195
## ENSG00000134532 -0.09648449 0.6567023 -3.243360 1.871623 0.01625704
## p method
## (Intercept) 0.8291957 2
## ENSG00000152894 0.4524498 2
## ENSG00000186073 0.6910811 2
## ENSG00000112232 0.4073748 2
## ENSG00000173898 0.3773421 2
## ENSG00000058091 0.3400477 2
## ENSG00000084072 0.8529838 2
## ENSG00000166833 0.4502926 2
## ENSG00000101057 0.4907455 2
## ENSG00000271550 0.8542568 2
## ENSG00000134532 0.8985422 2
##
## Method: 1-Wald, 2-Profile penalized log-likelihood, 3-None
##
## Likelihood ratio test=16.91808 on 10 df, p=0.07619644, n=24
## Wald test = 11.02228 on 10 df, p = 0.3557845
## logistf(formula = formula_str, data = as.data.frame(final_model_matrix),
## method = "detect_separation")
## Model fitted by Penalized ML
## Confidence intervals and p-values by Profile Likelihood
##
## Coefficients:
## (Intercept) ENSG00000152894 ENSG00000186073 ENSG00000112232 ENSG00000173898
## -0.12162486 0.80908839 -0.38336802 0.75311207 -0.71874510
## ENSG00000058091 ENSG00000084072 ENSG00000166833 ENSG00000101057 ENSG00000271550
## 0.93825921 -0.15907838 -0.41668886 -0.64074898 -0.19599018
## ENSG00000134532
## -0.09648449
##
## Likelihood ratio test=16.91808 on 10 df, p=0.07619644, n=24
## [1] "AUC (test): 1"
## [1] "Accuracy (test): 1"
Check direction of each gene in two models, all the same
| Modules (size) | Module correlation to treatment | Genes selected by lasso |
|---|---|---|
| lightgreen (152 genes) | Positive | 8 |
| darkred (63 genes) | Negative | 1 |
| midnightblue (303 genes) | Negative | 1 |
| ensembl_gene_id | external_gene_name | |
|---|---|---|
| 1 | ENSG00000058091 | CDK14 |
| 2 | ENSG00000084072 | PPIE |
| 3 | ENSG00000101057 | MYBL2 |
| 4 | ENSG00000112232 | KHDRBS2 |
| 5 | ENSG00000134532 | SOX5 |
| 6 | ENSG00000152894 | PTPRK |
| 7 | ENSG00000166833 | NAV2 |
| 8 | ENSG00000173898 | SPTBN2 |
| 9 | ENSG00000271550 | BNIP3P11 |
| 10 | ENSG00000186073 | CDIN1 |
## Warning: Setting row names on a tibble is deprecated.
## Setting row names on a tibble is deprecated.
Reactome
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KEGG
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